623 research outputs found
Simulated Tornado Optimization
We propose a swarm-based optimization algorithm inspired by air currents of a
tornado. Two main air currents - spiral and updraft - are mimicked. Spiral
motion is designed for exploration of new search areas and updraft movements is
deployed for exploitation of a promising candidate solution. Assignment of just
one search direction to each particle at each iteration, leads to low
computational complexity of the proposed algorithm respect to the conventional
algorithms. Regardless of the step size parameters, the only parameter of the
proposed algorithm, called tornado diameter, can be efficiently adjusted by
randomization. Numerical results over six different benchmark cost functions
indicate comparable and, in some cases, better performance of the proposed
algorithm respect to some other metaheuristics.Comment: 6 pages, 15 figures, 1 table, IEEE International Conference on Signal
Processing and Intelligent System (ICSPIS16), Dec. 201
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A Metaheuristic Adaptive Cubature Based Algorithm to Find Bayesian Optimal Designs for Nonlinear Models
Finding Bayesian optimal designs for nonlinear models is a difficult task because the optimality criteriontypically requires us to evaluate complex integrals before we perform a constrained optimization. Wepropose a hybridized method where we combine an adaptive multidimensional integration algorithm anda metaheuristic algorithm called imperialist competitive algorithm to find Bayesian optimal designs. Weapply our numerical method to a few challenging design problems to demonstrate its efficiency. Theyinclude finding D-optimal designs for an item response model commonly used in education, Bayesianoptimal designs for survivalmodels, and Bayesian optimal designs for a four-parameter sigmoid Emax doseresponse model. Supplementary materials for this article are available online and they contain an R packagefor implementing the proposed algorithm and codes for reproducing all the results in this paper
Optimization of object tracking based on enhanced imperialist competitive algorithm
Object tracking is one of the most challenging tasks in the field of computer vision. Tracking moving object(s) in video/image frame sequences in cluttered scenes usually results in complications and hence performance degradation. This is attributable to complexity in partial and full object occlusions and scene illumination changes which render object tracking complicated besides the delay in processing of moving images from frame to frame as well as the presence of multiple objects in the video frames under consideration. This paper explores the use of Enhanced Imperialist Competitive Algorithm (EICA) to track moving object(s) in video frames. The results obtained reveal the usefulness of this approach and provide the needed stimulus for further research in the problem domain.Keywords: Imperialist, Optimization, Tracking, Colony, Objec
Predicting arsenic and heavy metals contamination in groundwater resources of Ghahavand plain based on an artificial neural network optimized by imperialist competitive algorithm
Background: The effects of trace elements on human health and the environment gives importance to
the analysis of heavy metals contamination in environmental samples and, more particularly, human
food sources. Therefore, the current study aimed to predict arsenic and heavy metals (Cu, Pb, and Zn)
contamination in the groundwater resources of Ghahavand Plain based on an artificial neural network
(ANN) optimized by imperialist competitive algorithm (ICA).
Methods: This study presents a new method for predicting heavy metal concentrations in the
groundwater resources of Ghahavand plain based on ANN and ICA. The developed approaches were
trained using 75% of the data to obtain the optimum coefficients and then tested using 25% of the data.
Two statistical indicators, the coefficient of determination (R2) and the root-mean-square error (RMSE),
were employed to evaluate model performance. A comparison of the performances of the ICA-ANN and
ANN models revealed the superiority of the new model. Results of this study demonstrate that heavy
metal concentrations can be reliably predicted by applying the new approach.
Results: Results from different statistical indicators during the training and validation periods indicate
that the best performance can be obtained with the ANN-ICA model.
Conclusion: This method can be employed effectively to predict heavy metal concentrations in the
groundwater resources of Ghahavand plain.
Keywords: Neural networks (computer), Groundwater, Models, Algorithms, Trace element
Victoria Amazonica Optimization (VAO): An Algorithm Inspired by the Giant Water Lily Plant
The Victoria Amazonica plant, often known as the Giant Water Lily, has the
largest floating spherical leaf in the world, with a maximum leaf diameter of 3
meters. It spreads its leaves by the force of its spines and creates a large
shadow underneath, killing any plants that require sunlight. These water
tyrants use their formidable spines to compel each other to the surface and
increase their strength to grab more space from the surface. As they spread
throughout the pond or basin, with the earliest-growing leaves having more room
to grow, each leaf gains a unique size. Its flowers are transsexual and when
they bloom, Cyclocephala beetles are responsible for the pollination process,
being attracted to the scent of the female flower. After entering the flower,
the beetle becomes covered with pollen and transfers it to another flower for
fertilization. After the beetle leaves, the flower turns into a male and
changes color from white to pink. The male flower dies and sinks into the
water, releasing its seed to help create a new generation. In this paper, the
mathematical life cycle of this magnificent plant is introduced, and each leaf
and blossom are treated as a single entity. The proposed bio-inspired algorithm
is tested with 24 benchmark optimization test functions, such as Ackley, and
compared to ten other famous algorithms, including the Genetic Algorithm. The
proposed algorithm is tested on 10 optimization problems: Minimum Spanning
Tree, Hub Location Allocation, Quadratic Assignment, Clustering, Feature
Selection, Regression, Economic Dispatching, Parallel Machine Scheduling, Color
Quantization, and Image Segmentation and compared to traditional and
bio-inspired algorithms. Overall, the performance of the algorithm in all tasks
is satisfactory.Comment: 45 page
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